APLIC 2014 - Social Observatories Coordinating Network
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The document discusses the establishment of social observatories as regional data centers to address key social, behavioral, and economic science questions in the 21st century. It emphasizes the need for local, place-based studies to understand social inequalities and facilitate interdisciplinary collaboration in data collection and analysis. Through national coordination, these observatories aim to create a robust framework for gathering detailed data from various local contexts, enabling better policy-making and targeted interventions.
APLIC 2014 - Social Observatories Coordinating Network
- 1. Social Observatories: Envisioning Regional Data Centers for the 21st Century Sandra Hofferth University of Maryland April 30, 2014
- 2. The Challenge to SBE sciences Globalization Social media Declining response rates to surveys Rapid social and environmental changes Rapid shifts in the economy and in social groups Many current challenges are local
- 3. Key SBE Scientific Questions Opportunity and mobility: Place-based studies can help us better understand important issues such as the local sources of social inequality and disadvantage Place-based local studies can document the organization of neighborhoods and institutions, distribution and quality of schools, access to medical clinics and facilities, and employment opportunities in the informal sector Adaptation and Change: Place-based studies are better for examining responses to natural, economic, and social shocks (e.g., hurricane Katrina) – but we need to be in the communities before these events occur Behavior change: Studies of multiple places can contribute to our knowledge of the context in which micro-level behavior occurs: Could run experiments in different sites to see how the results vary across contexts. This has rarely been done.
- 4. Data for People and Places There is increased interest in linking different types of data, particularly to situate people in place. Many data sets do not provide the option to link with place at a fine (e.g. tract, neighborhood) level. Included are individual data with great detail or granularity. Other data come from a variety of sources – administrative, local land use, census. There is a need to collaborate across disciplines. We need tools to design better policy instruments that address human variability at the local level.
- 5. Origins of the Observatory Idea NSF sponsored some 8 workshops with members of the scientific community from 2005 to present, originally focusing on cyber-infrastructure for the social, behavioral and economic sciences Recent ones: December 2010, Oct. 2011, Feb 2012, and May 2012 Ten of us submitted a grant proposal to NSF for a Research Coordinating Network, the Social Observatories Coordinating Network (SOCN); we are funded for 3 years to obtain feedback from the scientific communities about this idea and produce a recommendation..
- 6. What is an Observatory? Each observatory or regional data center would be an entity, whether physical or virtual, that is charged with collecting, curating, and disseminating data from people, places, and institutions in the United States. These centers must provide a basis for inference from what happens in local places to a national context and ensure a robust theoretical foundation for social analysis.
- 7. Why the Observatory Approach? Observatories have a long history in the natural and ecological sciences, and they have served as points around which those communities have come together to strengthen their disciplines The large national longitudinal surveys can be thought of as a type of observatory, and have provided valuable data that are standardized and consistent but, with some exceptions, they cannot provide a detailed picture of a local area. AND there are growing problems with their ever rising costs, and with declining response rates across many of the surveys AND many questions of interest to parts of SBE disciplines are not addressed by these surveys
- 8. How will the Observatories be Nationally Representative? To accomplish these objectives, we propose to embed these regionally-based data centers in a nationally representative population-based sample that would enable the observatory data to be aggregated in such a way as to produce a national picture of the United States on an ongoing basis.
- 9. The Basic Problem People are highly concentrated in places Many places have few people Key question: Do you use place or population as the basis of the design?
- 11. Our Proposal: A sample of about 400 census tracts would be selected to represent the U.S. population while also fully capturing the diversity that characterizes local places. A unified centralized framework but distributed model Each observatory would be responsible for gathering information in a preselected set of census tracts The entire set of information gathered by all the observatories would provide a national sample to address core questions common across the observatories. In addition, each regional observatory could develop a set of priorities for research that differ from those of other observatories.
- 12. What types of data could be collected? Administrative sources (to identify people within tracts): Voting records, USPS address files Motor vehicle files Reverse phone directories Vital statistics Wage files Credit card data, Medicaid/welfare/food stamps data Data from sensors – air quality, noise, smartphones, time, exposures, distance Aggregate census data for tracts Survey data, ethnographic data, experiments Social media data (location-specific) Census/ACS for validation
- 13. How would the observatory system facilitate access to all its data? We will examine different models of data sharing and confidentiality - from restricted access (Census RDCs) to remote access and contracts. We will be holding a workshop to address the issues of confidentiality in data sharing and linking.
- 14. Examples of centers and applications Chicago, Il National Neighborhood Indicators Project Portland, Oregon Household Environmental Impacts and Exposure
- 15. Integrated Database of Child and Family Programs in Illinois Robert Goerge, Urban Center for Computation and data, Chapin Hall, University of Chicago
- 16. Overall Objective To reduce the burden of multi-problem families who contribute the most (86%) to cost of social services in the city: Unemployed parents Low socioeconomic status Welfare program participants Single-parent families Mothers who had their first child as an adolescent
- 17. Chapin Hall Integrated Data Base 1990-present Schools - PreK, Head Start, Public schools UI wage and benefit records SSA, TANF, SNAP, Child care subsidies Foster care, child maltreatment Medicaid providers, claims, population CPD arrests, juvenile court, incarceration
- 18. Method: Improved Targeting Through an extensive mapping process, the city knows exactly where the bulk of problematic families live. They are concentrated in a few census tracts in the city Can focus on those areas for programs.
- 19. National Neighborhood Indicators Partnership (NNIP) Thomas Kingsley Urban Institute
- 20. NNIP Collaborative effort since 1995 1. Urban Institute & local partners; now 37 cities 2. All partners build and operate neighborhood level information systems; administrative data from multiple sources Purpose 1. Strengthening neighborhoods 2. Promoting collaboration 3. Improving local decision-making
- 21. National Neighborhood Indicators PartnersAtlanta Austin Baltimore Boston Camden Chattanooga Chicago Cleveland Columbus Dallas Denver Des Moines Detroit Grand Rapids Hartford Indianapolis Kansas City Louisville Memphis Miami Milwaukee Minneapolis-St. Paul Nashville New Haven New Orleans New York City Oakland Philadelphia Pittsburgh Portland Providence Sacramento Saint Louis San Antonio Seattle Washington, DC
- 22. Neighborhood level – social/economic/physical Employment Births, deaths Crimes TANF, Food Stamps Child care Health Schools Property sales & prices Property ownership Code violations Assessed values Tax arrears Vacant/abandoned housing City/CDC plans Parcel level – physical/economic
- 24. Coalition for a Livable Future: Greater Portland Pulse Project Institute of Metropolitan studies University of Portland Meg Merrick, PhD
- 25. Background & Objectives • University-community partnership • Created a relational database infrastructure housed at Portland state – Regional Equity Atlas – 111 variables and 64 indicators in ‘real time’ • For bi-state regional planning • Interactive web tool and download capacity • Expansion for state-level efforts
- 27. Linking Local Consumption to Global Environmental Impacts Klaus Hubacek Department of Geography University of Maryland
- 28. Objectives Understanding of what drives peoples’ consumption activities Estimating household environmental impacts and exposure Locally and globally
- 29. Linking Local Consumption to Global Impacts
- 32. What are the Barriers? Differences in data collection from place to place, state to state Data alignment between projects, linking database architectures, web services, people Human capital in government to do this Legal problems in sharing data
- 33. “Good luck getting the data sharing agreement through our lawyers….”
- 34. Benefits of this Approach But – many of the problems have been locally worked out. All three examples successfully engaged their communities and met community needs. Along with a national coordinating center this model could be a valuable contribution to the national data infrastructure.
- 35. What do we gain from this platform? A national framework for studying local contexts for social dynamics A national SBE cyberinfrastructure to serve 21st century society A national framework for interdisciplinary collaboration
- 36. Thank you Sandra Hofferth hofferth@umd.edu http://socialobservatories.org